EARLY DIAGNOSIS OF DIABETIC FOOT ULCERS USING AI-BASED IMAGE CLASSIFICATION TECHNIQUES

Abstract: Diabetic foot ulcers (DFUs) are an advanced complication associated with diabetes mellitus which, if not diagnosed promptly, can lead to severe infections, amputations, and even death. Traditional methods of diagnosis rely heavily on the visual checks by medical personnel, which often is not timely. Detecting DFUs using various forms of AI (Artificial Intelligence) offers an efficient, automated, and timely solution. In this study, we propose a complete framework with AI techniques that underset the deep learning structure, especially convolutional neural networks (CNNs), for the classification and prediction of diabetic foot ulcers. The model employs a set of DFU images to train and validate the model's performance on numerous defined metrics including, but not limited to, accuracy, precision, recall, and F1-score. The experiments performed show the significant role AI can play in improving early DFU detection, a shift that would revolutionize the care of diabetic foot issues and improve healthcare outcomes.

Keywords: Diabetic Foot Ulcer (DFU), Artificial Intelligence (AI), Convolutional Neural Network (CNN), Deep Learning, Early Diagnosis, Medical Imaging.